
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "12"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 85 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 81 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	85.898218	83.052319	145.503973	146.736807	37.118128	31.555349	78.637281	87.116265	0.0189	0.0180	0.0009	1.116449	1.113311
2459593	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	79.695297	73.698679	179.196695	158.269952	59.154819	58.899351	237.044674	151.560981	0.0197	0.0181	0.0005	1.114723	1.119184
2459592	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	6.366103	12.384296	61.280285	62.520228	81.156395	63.640315	78.902148	84.348328	0.0202	0.0192	0.0015	nan	nan
2459591	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	86.836206	81.946656	187.116274	196.685407	60.075795	73.199843	208.680785	285.441321	0.0195	0.0178	0.0016	1.106462	1.100604
2459590	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	184.733427	102.821742	196.756402	167.365654	55.157782	51.800068	332.595640	224.803407	0.0182	0.0184	0.0007	1.125696	1.133346
2459589	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	339.194220	338.592283	inf	inf	49.463651	53.691614	366.930951	381.393622	nan	nan	nan	0.000000	0.000000
2459588	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	118.366516	71.590119	239.450052	219.932668	43.398095	55.357986	94.072952	95.613537	0.0180	0.0176	0.0006	1.124648	1.126861
2459587	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	175.200413	118.993402	342.575692	303.228868	86.791792	62.402315	617.904499	479.668519	nan	nan	nan	0.000000	0.000000
2459586	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	314.169082	315.679685	inf	inf	11.550973	11.604600	573.713738	580.535356	nan	nan	nan	0.000000	0.000000
2459585	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	61.530019	62.746984	43.069475	47.969156	9.132887	8.729209	595.128077	699.053696	0.0192	0.0179	0.0010	0.000000	0.000000
2459584	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	74.155150	92.922229	207.491617	205.222667	18.758918	15.919173	72.332089	72.278708	0.0193	0.0186	0.0010	0.000000	0.000000
2459583	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	40.943846	60.554299	57.988831	69.955865	33.426932	57.523868	141.372934	287.259171	0.0193	0.0173	0.0013	1.121645	1.110638
2459581	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	79.327069	78.478171	78.780583	77.275676	26.711859	51.085380	300.276884	288.950547	0.0190	0.0177	0.0015	1.102472	1.100960
2459580	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	85.366970	75.485330	79.969198	80.583766	31.103983	51.267258	234.223643	282.610588	0.0192	0.0179	0.0008	1.110831	1.109878
2459579	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	42.680604	57.286725	77.310378	77.236973	41.821477	27.529857	362.650001	268.106512	0.0191	0.0183	0.0006	1.137645	1.138638
2459578	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	6.430461	8.958909	25.871166	29.076162	95.871492	137.955486	446.892020	779.306813	0.0198	0.0188	0.0007	nan	nan
2459577	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	81.407115	93.635370	70.599887	74.044178	56.997314	47.713644	253.952005	323.151712	0.0190	0.0176	0.0005	1.097676	1.094612
2459576	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	71.519515	68.162850	53.268738	57.562269	26.757005	45.524317	273.007473	345.812598	0.0190	0.0172	0.0013	1.084479	1.078153
2459575	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	55.680489	51.334572	58.314912	50.415875	29.316917	23.429337	42.711330	26.745502	0.0191	0.0185	0.0006	0.000000	0.000000
2459574	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	50.118606	68.821034	50.775367	56.985693	38.559788	50.608141	132.696199	211.748599	0.0186	0.0178	0.0007	1.046643	1.041995
2459573	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	90.028504	166.318947	96.916836	128.373271	46.204533	104.858626	403.365802	712.844220	0.0189	0.0173	0.0014	0.000000	0.000000
2459572	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	103.212216	56.534265	66.416803	49.205232	58.118335	23.817502	958.576254	461.787295	0.0183	0.0182	0.0010	1.116224	1.124625
2459571	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	6.282974	7.367317	28.927889	30.002837	100.772359	112.302111	615.187698	789.901852	0.0195	0.0188	0.0006	nan	nan
2459570	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	68.527974	75.729861	69.571758	73.325068	17.407488	20.722754	402.995859	429.771280	0.0183	0.0175	0.0008	0.000000	0.000000
2459569	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	40.020966	61.594991	59.872719	58.390101	13.321494	13.796480	20.053423	21.614740	0.0193	0.0187	0.0006	1.057392	1.054789
2459566	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	56.550681	78.226603	69.852993	76.317361	38.219222	45.517782	203.203792	231.170849	0.0191	0.0174	0.0013	0.917846	0.919131
2459565	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	0.674491	0.674491	0.714579	0.714579	1.371796	1.371796	0.680147	0.680147	nan	nan	nan	0.000000	0.000000
2459564	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	50.527227	105.554018	63.440695	92.050766	7.143218	19.615334	111.560401	413.100384	0.0191	0.0171	0.0024	1.110612	1.096583
2459563	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	61.576265	66.337670	125.143848	132.153881	22.708145	37.780330	206.008802	241.657409	0.0190	0.0182	0.0006	0.000000	0.000000
2459562	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459561	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	40.385911	43.684593	33.516078	34.553380	9.416294	7.153475	288.012916	278.380876	0.0184	0.0178	0.0005	1.082280	1.079683
2459560	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	41.807341	45.806950	32.748829	32.051729	5.422375	7.744035	173.891110	167.528614	0.0186	0.0182	0.0015	1.010571	1.004171
2459559	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459558	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	70.515646	63.970858	69.197239	71.891663	31.683216	39.727853	304.430857	311.814035	0.0189	0.0176	0.0008	1.142503	1.137656
2459557	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0193	0.0184	0.0007	nan	nan
2459556	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	103.790848	89.829654	85.503786	92.516494	35.950465	78.338810	846.454525	1080.816913	0.0182	0.0172	0.0009	1.131927	1.126974
2459554	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0182	0.0179	0.0006	nan	nan
2459553	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0182	0.0173	0.0012	nan	nan
2459552	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	71.394429	68.984993	96.774391	97.061937	60.325180	52.213730	241.030190	286.118845	0.0188	0.0178	0.0008	0.000000	0.000000
2459551	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459550	RF_maintenance	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0324	0.0319	0.0005	nan	nan
2459549	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	66.767706	60.357557	81.148011	73.204650	48.636106	42.791069	478.682996	308.055466	0.0185	0.0182	0.0006	1.133643	1.136412
2459542	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	67.734951	75.377586	161.768607	180.362842	3.987429	7.842538	314.492227	527.250312	0.0179	0.0171	0.0011	1.122544	1.115239
2459541	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.421044	10.990935	46.223063	43.336227	664.979200	615.443201	29.710743	38.997876	0.0190	0.0181	0.0008	1.107414	1.100697
2459540	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	69.708331	59.528608	35.996867	37.002674	16.713806	23.550238	115.461094	164.567265	0.0186	0.0175	0.0009	1.138083	1.136276
2459536	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	75.902687	96.130837	122.203486	127.103671	43.821281	54.318065	496.489072	565.703875	0.0188	0.0178	0.0006	1.145989	1.141891
2459535	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	36.686302	45.530628	31.314690	30.323922	9.768729	14.082122	126.558401	135.872869	0.0184	0.0182	0.0007	1.130011	1.136159
2459534	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	51.341060	43.382481	35.308272	28.037543	37.592847	16.386912	244.476856	116.533700	0.0183	0.0182	0.0010	1.128827	1.138367
2459533	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	69.519948	78.419288	77.141093	71.924307	31.498576	36.931255	452.482854	437.003706	0.0186	0.0180	0.0007	1.138135	1.140706
2459532	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	128.703950	80.468916	118.549577	95.297866	72.610618	59.644231	545.724953	287.441175	0.0189	0.0176	0.0007	1.132229	1.137571
2459530	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	40.288207	43.618639	30.113734	29.168073	12.919862	13.695137	189.084899	162.478201	0.0186	0.0181	0.0006	1.123805	1.123832
2459527	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	65.007341	62.527764	110.017063	97.454995	90.806225	63.226681	226.487217	151.889262	0.0185	0.0178	0.0010	0.000000	0.000000
2459523	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	118.291644	87.097793	125.182592	98.244383	121.351199	49.496663	727.625179	302.868363	nan	nan	nan	1.376309	1.387693
2459522	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	63.027366	68.946505	101.730003	95.806565	79.266223	109.748358	423.073431	343.422064	0.0187	0.0185	0.0007	1.190651	1.189269
2459513	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	8.054302	9.764881	30.982045	25.787091	144.611514	90.775997	719.905527	397.253796	0.0199	0.0198	0.0010	nan	nan
2459508	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	49.421935	63.368119	89.062039	92.525790	31.339228	30.861346	41.660143	52.147107	0.0189	0.0180	0.0007	0.000000	0.000000
2459505	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	80.636889	81.166353	98.036664	102.899727	106.437531	93.875790	517.147075	471.799660	0.0118	0.0105	0.0028	1.143827	1.153407
2459503	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	88.944509	76.167824	175.091522	150.445499	62.175965	53.195789	641.147669	307.374350	0.0092	0.0091	0.0052	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 12 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 12, Node 0:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 12: 2459609

Antenna 12: 2459608

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Antenna 12: 2459566

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Antenna 12: 2459524

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Antenna 12: 2459522

Antenna 12: 2459513

Antenna 12: 2459508

Antenna 12: 2459506

Antenna 12: 2459505

Antenna 12: 2459503

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶